Embodiments described herein generally relate to the selection of presets for the visualization of image data sets.
In the medical field, two-dimensional (2D) and three-dimensional (3D) image data sets are collected by a variety of techniques—referred to as modalities in the field—including conventional X-ray, computer-assisted tomography (CT), magnetic resonance (MR), ultrasound and positron-emission-tomography (PET). Examples of 2D images include not only conventional X-ray images, but also 2D images derived from 3D image data sets, i.e. volume data sets, such as a “slice” of a CT scan or a “slab” of volume data from a CT scan in a multi-planar reformatting (MPR) view. Time resolved 3D studies are also well known and are usually referred to as 4D studies, with time being the fourth “dimension”. For example, time-resolved perfusion in abdominal organs is measured using 4D dynamic contrast enhanced CT (DCE-CT). It is also known to co-present images of the same patient taken with different modalities, such as combining CT and PET scans into a single image. These combined representations are sometimes referred to as 5D studies.
In a visualization application, images presented to the user are represented by a gray scale, there being one gray scale in a monochrome representation and multiple gray scales in color representations. For the sake of simplicity of explanation, we assume monochrome representation in the following. The gray scale values map back to the scanner's originally sampled image value (e.g. in Hounsfield Units (HUs)). It is a known fact that human vision cannot distinguish very well between different shades of gray. It has been said that a person can, at most, only perceive about 20-30 different levels of gray at a time. Therefore, it is often helpful to confine the gray scale to a relatively small range of signal values which contains the tissue type of interest, rather than attempting to visualize the full scale at once. The restricted range of the gray scale is conventionally defined in terms of a “window width” and a “window level”. The window level defines the center point within the range of signal values to be displayed. The window width is the range of densities around this center point to be displayed as a gray scale value from white to black. In a monochrome representation, densities which fall below this range will be displayed as black, and densities above this scale will be displayed as white. For example, in a CT data set, bone is known to be dense and hence have high values of Hounsfield Unit (HU). To provide a good visualization of bone features in a CT data set, the window level is therefore set to a high value, e.g. 350. The window width is set to be relatively large, since bone tissue is known to span a large range of HUs, e.g. 1200.
A particular combination of window level and window width known to be suitable for visualizing particular tissue types or other features is referred to in the field as a “preset”. A preset thus defines a mapping between image values in an image data set and what shade pixels or voxels having those image values are given when displayed to a user. For any given modality, a variety of presets will be stored and available for user selection. For example, if the user has a CT data set loaded, the user can switch between different presets suitable for showing different organs, blood vessels, bone and so forth. While the term preset probably originated because of the fact that window settings were pre-determined and stored for user selection, somewhat confusingly the term preset is now used more generally in the field to refer to any set of visualization parameters, even if the set is not pre-determined. For example, some applications adjust or set window settings on-the-fly during a user viewing session. The term preset in this document is used in this industry standard way to include both pre-determined and on-the-fly-determined visualization parameters.
Over time, within any given modality a wide variety of clinical uses has developed. For example, CT scanning may be used to study bone and various types of tissue from relatively hard to relatively soft. In CT, the values of the voxels contained in the volume data set can span a very wide range. Consequently large numbers of presets have been found, each optimized for particular clinical uses. Moreover, many CT and MR studies use contrast agents to highlight features of clinical interest. There are many different types of contrast agent with different properties for the relevant modality. For example, there may be a preset optimized for viewing angiograms taken with a particular contrast agent. These clinical application specific, pre-optimized visualization parameters are typically stored and made available to users as elements in a library of presets with clinically relevant names to assist a user making an appropriate selection of preset. Users can also make their own presets and store them in the library, either ab initio or by modifying existing presets.
An example list of presets for 2D X-ray images or MPR views of volume data sets acquired by CT might be:                1) Abdomen        2) Abdominal Angio        3) Angio        4) Bone (Hard)        5) Bone (Soft)        6) Calcium        7) Fat        8) Liver        9) Liver (Contrast)        10) Lung        11) Mediastinum        12) Thoracic Angio        13) Head        
As clinical knowledge becomes ever more developed and specialized, more and more presets are defined and stored, and as a result it becomes ever more difficult for a user to make the most appropriate choice of preset from the large number available. For example, the choice of preset will depend on the subject, what type of data is being represented, whether (and if so, how) the data are calibrated and what particular features of the image data set the user might wish to highlight, which will depend on the clinical application.